def __init__(self,**config):
# 设置参数，必要参数
self.raw_variables = config.get('variables')
self.variables = list(set(self.raw_variables.keys()))
self.assist_variables = set(self.raw_variables.values())
self.assist_variables.discard(None)
self.assist_variables = list(self.assist_variables)
self.region = config.get('region')
self.year = config.get('year')
self.scale = config.get('scale')
self.report_title = config.get('report_title')
self.report_path = config.get('report_path')
# 设置参数，非必要参数
if 'report_author' in config.keys():
self.report_author = config.get('report_author')
else:
self.report_author = 'Plutoese'
# 区域数据
self.region_data = RegionData()
# 查询数据
if (self.region is not None) and (self.year is not None):
print(self.variables)
self._data = self.region_data.find(region=self.region,year=self.year,variable=self.variables,scale=self.scale)
self._assist_data = self.region_data.find(region=self.region,year=self.year,variable=self.assist_variables,scale=self.scale)
elif self.region is not None:
self._data = self.region_data.find(region=self.region,variable=self.variables,scale=self.scale)
self._assist_data = self.region_data.find(region=self.region,variable=self.assist_variables,scale=self.scale)
elif self.year is not None:
self._data = self.region_data.find(year=self.year,variable=self.variables,scale=self.scale)
self._assist_data = self.region_data.find(year=self.year,variable=self.assist_variables,scale=self.scale)
else:
self._data = self.region_data.find(variable=self.variables,scale=self.scale)
self._assist_data = self.region_data.find(variable=self.assist_variables,scale=self.scale)
self.data = self._data['pdata']
self.year = list(self.data.items)
# 格式转换
for y in self.year:
self.data[y][self.variables] = self.data[y][self.variables].astype(np.float64)
self.assist_data = self._assist_data['pdata']
# 生成报告
self.report = Report(title=self.report_title,author=self.report_author)
class RegionReportGenerator():
'''类RegionReportGenerator是区域分析报告生成器
:param dict config: 参数配置
'''
# 构造函数
def __init__(self,**config):
# 设置参数，必要参数
self.raw_variables = config.get('variables')
self.variables = list(set(self.raw_variables.keys()))
self.assist_variables = set(self.raw_variables.values())
self.assist_variables.discard(None)
self.assist_variables = list(self.assist_variables)
self.region = config.get('region')
self.year = config.get('year')
self.scale = config.get('scale')
self.report_title = config.get('report_title')
self.report_path = config.get('report_path')
# 设置参数，非必要参数
if 'report_author' in config.keys():
self.report_author = config.get('report_author')
else:
self.report_author = 'Plutoese'
# 区域数据
self.region_data = RegionData()
# 查询数据
if (self.region is not None) and (self.year is not None):
print(self.variables)
self._data = self.region_data.find(region=self.region,year=self.year,variable=self.variables,scale=self.scale)
self._assist_data = self.region_data.find(region=self.region,year=self.year,variable=self.assist_variables,scale=self.scale)
elif self.region is not None:
self._data = self.region_data.find(region=self.region,variable=self.variables,scale=self.scale)
self._assist_data = self.region_data.find(region=self.region,variable=self.assist_variables,scale=self.scale)
elif self.year is not None:
self._data = self.region_data.find(year=self.year,variable=self.variables,scale=self.scale)
self._assist_data = self.region_data.find(year=self.year,variable=self.assist_variables,scale=self.scale)
else:
self._data = self.region_data.find(variable=self.variables,scale=self.scale)
self._assist_data = self.region_data.find(variable=self.assist_variables,scale=self.scale)
self.data = self._data['pdata']
self.year = list(self.data.items)
# 格式转换
for y in self.year:
self.data[y][self.variables] = self.data[y][self.variables].astype(np.float64)
self.assist_data = self._assist_data['pdata']
# 生成报告
self.report = Report(title=self.report_title,author=self.report_author)
# 变量转换
def variable_transformation(self):
generator_data = dict()
self.generator_variables = set()
for year in list(self.data.items):
assist_data_year = self.assist_data[year]
del assist_data_year['region']
total_data = pd.merge(self.data[year], assist_data_year, left_index=True, right_index=True, how='outer')
csdexplorer = CrossSectionRegionDataExplorer(total_data)
cross_section_frame = pd.DataFrame({'region':self.data[year]['region']})
for var in self.data[year].columns[1:]:
variable_name = var
dframe = pd.DataFrame({variable_name:self.data[year][variable_name]})
if self.raw_variables[var] is not None:
variable_name = '|'.join([var,self.raw_variables[var]])
dframe = csdexplorer.per_variable(pop=self.raw_variables[var],var=[var])
if (np.min(dframe[variable_name]) > 0) and (np.max(dframe[variable_name]) < 1):
dframe = dframe.applymap(lambda x: 100 * x)
cross_section_frame = pd.merge(cross_section_frame, dframe, left_index=True, right_index=True, how='outer')
continue
if (np.min(dframe[variable_name]) > 0) and (np.max(dframe[variable_name]) < 100):
cross_section_frame = pd.merge(cross_section_frame, dframe, left_index=True, right_index=True, how='outer')
continue
if np.min(dframe[variable_name]) > 0:
dframe = dframe.applymap(np.log)
dframe.columns = ['|'.join([variable_name,u'对数'])]
#print(dframe.columns)
cross_section_frame = pd.merge(cross_section_frame, dframe, left_index=True, right_index=True, how='outer')
generator_data[year] = cross_section_frame
self.generator_variables.update(list(cross_section_frame.columns))
self.generate_data = pd.Panel(generator_data)
for y in self.year:
self.generate_data[y][list(self.generate_data[y].columns)[1:]] = self.generate_data[y][list(self.generate_data[y].columns)[1:]].astype(np.float64)
self.generator_variables = list(self.generator_variables)
self.generator_variables.remove('region')
# 分析的主接口
def analysis(self):
#self.describe()
#self.describe(data_source='generated')
self.corr()
#self.corr(data_source='generated')
# 相关性分析
def corr(self,data_source='Raw',model='auto'):
if re.match('^Raw$',data_source):
variables = self.variables
data = self.data
else:
variables = self.generator_variables
data = self.generate_data
for year in self.year:
self.report.create_section(year)
data_frame = data[year]
cse = CrossSectionRegionDataExplorer(data_frame)
corr = cse.corr()
corr2 = corr.applymap(abs)
corr_rank = corr2.rank(method='max',ascending=False)
print(corr)
print(corr.shape[0],len(variables))
print(corr_rank)
top_number = min(len(variables)+1,11)
for var in variables:
self.report.create_subsection(var)
#print(corr[var])
corr_data = {'variable':corr_rank.index,'rank':corr_rank[var],'corr':corr[var]}
corr_data_frame = pd.DataFrame(corr_data,columns=['rank','variable','corr'])
corr_data_frame = corr_data_frame.set_index('rank')
corr_data_frame = corr_data_frame.sort_index()
corr_data_frame = corr_data_frame.reindex(list(range(2,len(variables)+1)))
corr_data_frame = corr_data_frame.set_index('variable')
corr_data_frame = corr_data_frame.applymap(lambda x:'{0:.2f}'.format(x))
corr_data_frame = corr_data_frame.iloc[:top_number]
table_data = self.dataframe_to_list(corr_data_frame)
self.report.add_table(table_data['data'],table_data['nrow'],table_data['ncol'])
for xvar in corr_data_frame.index:
plt = cse.scatter(y=var,x=xvar,save=True)
self.report.add_matplotlib_graph(plt,caption='图形')
plt.close()
print('-------------------------')
print(corr_data_frame)
print(self.dataframe_to_list(corr_data_frame))
print('-------------------------')
# 描述性统计
def describe(self,data_source='Raw'):
if re.match('^Raw$',data_source):
variables = self.variables
data = self.data
else:
variables = self.generator_variables
data = self.generate_data
for var in variables:
# 生成章节
self.report.create_section(var)
for year in self.year:
# 生成分支章节
self.report.create_subsection(year)
#data_frame = pd.DataFrame({var:data[year][var]})
data_frame = data[year][['region',var]]
cse = CrossSectionRegionDataExplorer(data_frame)
#print(cse.describe().applymap(lambda x:'{0:.2f}'.format(x)))
tdata = cse.describe().applymap(lambda x:'{0:.2f}'.format(x))
tdata = self.dataframe_to_list(tdata)
print(var,year)
self.report.add_table(tdata['data'],tdata['nrow'],tdata['ncol'])
# 生成pdf报告
def generate_pdf(self):
self.report.flush()
self.report.generate_pdf(self.report_path)
# 把data.frame转化为list
def dataframe_to_list(self,dframe):
dict_data = dframe.to_dict('split')
result = dict_data['data']
[result[i].insert(0,dict_data['index'][i]) for i in range(len(result))]
title = [u'变量']
title.extend(dict_data['columns'])
result.insert(0,title)
nrow = len(result)
ncol = len(result[0])
return {'data':result,'ncol':ncol,'nrow':nrow}
